CN-122026519-A - Moon optimal configuration method of electric hydrogen energy supplementing station considering new energy consumption

CN122026519ACN 122026519 ACN122026519 ACN 122026519ACN-122026519-A

Abstract

The invention discloses a month optimal configuration method of an electro-hydrogen energy supplementing station considering new energy consumption, which comprises the following steps: firstly, taking the source side uncertainty of wind power and photovoltaic and the charge side uncertainty of electricity replacement and hydrogenation into consideration, and constructing a multidimensional uncertainty basic scene through time sequence clustering. And secondly, further reducing the high-dimensional coupling probability scene by using an ISODATA algorithm according to the density distribution of the basic scene, and further generating a typical scene for optimizing the configuration model. Then, a self-adaptive multi-layer moving average filtering algorithm is adopted to decompose a wind-light combined power curve, a high-frequency component is configured for optimizing a charging and changing strategy of the power changing station, and a low-frequency component is configured for the hydrogen producing station. And finally, the optimal configuration model of the electro-hydrogen energy supplementing station takes the monthly comprehensive operation cost of the energy supplementing station as an optimal target, and the configuration result is solved by Gurobi to obtain the configuration scheduling strategy of the energy supplementing station. The invention can realize the on-site consumption of wind and light generating capacity with larger capacity and has high reliability.

Inventors

Tang Rongchuan
NIE JIA
JIANG ANNI
LIU HAOYU
LI YICHENG
LI PEI
YIN SIJIA
GE ANTONG
NI CHAO
FU XIANGYANG
LIU WEIYE
XIE LING
LONG JINGYAN

Assignees

国网江苏省电力有限公司扬州供电分公司

Dates

Publication Date: 20260512
Application Date: 20260211

Claims (10)

1. The month optimal configuration method of the electro-hydrogen energy supplementing station considering new energy consumption is characterized by comprising the following steps of: s1, acquiring historical time sequence output data of a photovoltaic and wind power system to be consumed controllably in a regional range, and acquiring historical time sequence demand data of power conversion and hydrogenation of a user in the regional range; S2, clustering historical time sequence output data of the wind power system, historical time sequence output data of the photovoltaic system, historical time sequence demand data of power conversion and historical time sequence demand data of hydrogenation by adopting a clustering algorithm to obtain an optimal clustering result; S3, constructing and generating a multidimensional uncertainty coupling probability lattice and the number M of coupling scenes according to the optimal clustering result; s4, according to the multidimensional uncertainty coupling probability lattice, reducing the number M of the coupling scenes into N typical daily scenes, and weighting and equivalent N typical day show scenes into typical month scenes according to scene probability; s5, sequentially carrying out decomposition operation on wind power and photovoltaic curve data in a typical month scene by adopting a moving average filtering algorithm; and S6, based on the decomposition data, constructing an electric hydrogen energy supplementing station optimal configuration model considering hydrogen energy transregional scheduling and electricity changing hydrogenation user perception by taking the lowest monthly comprehensive operation cost of the energy supplementing station as an optimization target, and obtaining a configuration scheduling strategy of the energy supplementing station by adopting Gurobi to solve a configuration result.
2. The method for optimizing the configuration of the electro-hydrogen energy supplementing station by taking new energy consumption into consideration according to claim 1, wherein the method comprises the steps of, In the step S2, clustering is carried out according to a time-of-day scale by adopting a K-means clustering algorithm, and four independent probability scenes are expressed as a set form: Wind power scenes { (P w,i (t), p w,i )| 1≤t≤T, 1≤i≤K w }, wherein P w,i (T) is a daily power curve of wind power in an ith scene, P w,i is a probability of the ith scene of wind power, K w is an optimal clustering number of the wind power scenes, T is a daily period index, and T represents the maximum value of the daily period index T; The photovoltaic scene { (P pv,j (t), p pv,j )| 1≤t≤T, 1≤j≤K pv }, wherein P pv,j (t) is a solar power curve of the photovoltaic in a j-th scene, P pv,j is light Fu Di j scene probabilities, and K pv is the optimal clustering number of the photovoltaic scenes; The power change scene is { (P s,k (t), p s,k )| 1≤t≤T, 1≤k≤K s }, wherein P s,k (t) is a daily power curve of power change in the kth scene, P s,k is the probability of the kth scene of power change, and K s is the optimal clustering number of the power change scene; the hydrogenation scene { (P h,l (t), p h,l )| 1≤t≤T, 1≤l≤K h }, wherein P h,l (t) is a daily power curve of hydrogenation in the first scene, P h,l is the probability of the first scene of hydrogenation, and K h is the optimal cluster number of the hydrogenation scene.
3. The method for optimizing the configuration of the electro-hydrogen energy supplementing station by taking new energy consumption into consideration according to claim 2, wherein, In step S3, the multidimensional uncertainty coupling probability lattice includes independent scene mutual coupling, specifically as follows: The coordinates of the mth coupling scene in the probability space are denoted as (p w,i ,p pv,j ,p s,k ,p h,l ), the corresponding spatial density is characterized as p m (p w,i ,p pv,j ,p s,k ,p h,l ) = p w,i× p pv,j× p s,k× p h,l , Wherein p w,i is the ith scene probability of wind power, i is more than or equal to 1 and less than or equal to K w ,p pv,j is the Fu Di j scene probabilities of light, j is more than or equal to 1 and less than or equal to K pv ,p s,k is the kth scene probability of power conversion, K is more than or equal to 1 and less than or equal to K s ,p h,l is the first scene probability of hydrogenation, and l is more than or equal to 1 and less than or equal to K h ; The number of the coupling scenes is M=K w ×K pv ×K s ×K h , and M is more than or equal to 1 and less than or equal to M.
4. The method for optimizing the configuration of the electro-hydrogen energy supplementing station by taking new energy consumption into consideration according to claim 1, wherein the method comprises the steps of, In the step S4 of the process, The coupling scene reduction comprises the steps of taking coordinates and numerical values of each coupling scene in a density space as clustering objects, and clustering M coupling scenes into N typical daily scenes by adopting a density clustering algorithm; The N typical day scenes after the clipping are equivalent to typical month scenes in 30 days per month.
5. The method for optimizing the configuration of the electro-hydrogen energy supplementing station by taking new energy consumption into consideration according to claim 1, wherein the method comprises the steps of, In the step S5 of the process, Setting a window of the moving average filtering as W and setting a fluctuation threshold as flu, and repeatedly carrying out the moving average filtering on the low-frequency components obtained by the moving average filtering in turn, wherein the obtained final low-frequency components P LF (d, t) are configured to a hydrogenation station, and the remaining high-frequency fluctuation components P HF (d, t) to be stabilized are configured to a power exchange station.
6. The method for optimizing configuration of the electro-hydrogen energy supplementing station by taking new energy consumption into consideration according to claim 1, wherein an objective function of the electro-hydrogen energy supplementing station optimizing configuration model is as follows: Wherein f is the monthly call comprehensive net cost of the electric hydrogen energy supplementing station, C clinet is the charging and changing perception cost of a user side, C H,express is the hydrogen transfer cost of the hydrogen adding station, C BS is the monthly equivalent operation cost of the hydrogen adding station, C HS is the monthly equivalent operation cost of the hydrogen preparing station, R BS is the monthly operation benefit of the hydrogen adding station, R HS is the monthly operation benefit of the hydrogen preparing station, and C Grid is the electricity purchasing cost of an external electric network.
7. The method for optimizing configuration of the electro-hydrogen energy supplementing station for taking new energy consumption into consideration according to claim 6, wherein, The user side charging and battery replacement perceptibility cost is as follows: ; In the formula, alpha, beta and gamma are model preset coefficients, SOC final (D, t) is the actual state of charge of a power conversion user after power conversion in a period of D days and t, and D represents the maximum value of a day index D in a month.
8. The method for optimizing configuration of the electro-hydrogen energy supplementing station for taking new energy consumption into consideration according to claim 6, wherein, The hydrogen transfer cost of the hydrogen adding station is as follows: ; Wherein m ex (d, t) is the hydrogen transportation quantity at the time of d days t, x (d, t) is the allocation quantity of hydrogen transportation vehicles at the time of d days t, Y 1 is the transportation quantity cost coefficient of the hydrogen transportation vehicles, and Y 2 is the allocation fixed cost coefficient of the hydrogen transportation vehicles.
9. The method for optimizing configuration of the electro-hydrogen energy supplementing station for taking new energy consumption into consideration according to claim 6, wherein, The equivalent operation cost of the monthly power exchange station is as follows: ; In the formula, Represents an aging cost loss coefficient of a single battery, η ch represents a charging efficiency of the battery, and P ch (num, d, t) represents a battery charging and discharging power of a num-th battery for a period of d days t; NUM represents the number of battery configurations in the current month of the battery exchange station; The monthly equivalent operation cost of the hydrogen production station is as follows: Wherein, C HS,inv represents the annual investment cost of the hydrogen production station, C HS,ope represents the annual operation maintenance cost of the hydrogen production station, C HS,serve represents the monthly service cost of the hydrogen production station, RD represents the actual operation days of the hydrogen production station; The yield of the power exchange station is as follows: Wherein r BS represents unit income of a power change increment of a power change user, and SOC (num, d, t) represents the state of charge of a num battery in a period of d days and t; Representing the optimally configured rated capacity of a single cell; the yield of the hydrogen production station is as follows: ; wherein r HS represents the unit gain of the increment of hydrogenation users; the hydrogen storage amount sold to the hydrogen automobile by the hydrogen station in the period of d days t is represented; the external electricity purchase cost is as follows: ; Wherein c Grid represents the unit price of electricity purchase, P BGrid (d, t) represents the electric power purchased from the off-grid of the exchange station in the period of d days t, and P HGrid (d, t) represents the electric power purchased from the off-grid of the hydrogen addition station in the period of d days t.
10. The method for monthly optimal configuration of the electric hydrogen energy supplementing station considering new energy consumption according to claim 1, wherein constraint conditions of an optimal configuration model of the electric hydrogen energy supplementing station comprise battery charge and discharge constraint, battery SOC constraint, hydrogen storage electrolyzer constraint and hydrogen storage SOH constraint.

Description

Moon optimal configuration method of electric hydrogen energy supplementing station considering new energy consumption Technical Field The invention belongs to the technical field of power systems, and particularly relates to a month optimization configuration method of an electro-hydrogen energy supplementing station considering new energy consumption. Background With the continuous increase of the duty ratio of renewable energy sources such as wind power, photovoltaic and the like in power generation side resources of a power system, the method is particularly important for grid-connected fluctuation management of the renewable energy sources with strong randomness and fluctuation. In recent years, renewable energy consumption methods based on light storage and replacement are developed as research subjects, most of the existing optimization ideas are based on uncertain data sets of new energy and reduced to a small number of typical scenes, and the final configuration method is obtained by weighting configuration results obtained by repeated optimization of each typical scene. The traditional thinking concentrates calculation force in the solving process of the optimal configuration, and the sum of solving time is increased by times for a complex optimization model. Meanwhile, the conventional electric energy storage has relatively low energy density, and cannot be fully consumed for large-capacity wind and light resources, so that the problem of wind and light abandoning is caused. Disclosure of Invention Aiming at the defects of the prior art, the invention aims to provide a month optimal configuration method of an electric hydrogen energy supplementing station considering new energy consumption. The technical scheme of the invention is that the month optimal configuration method of the electro-hydrogen energy supplementing station considering new energy consumption comprises the following steps: s1, acquiring historical time sequence output data of a photovoltaic and wind power system to be consumed controllably in a regional range, and acquiring historical time sequence demand data of power conversion and hydrogenation of a user in the regional range; S2, clustering historical time sequence output data of the wind power system, historical time sequence output data of the photovoltaic system, historical time sequence demand data of power conversion and historical time sequence demand data of hydrogenation by adopting a clustering algorithm to obtain an optimal clustering result; S3, constructing and generating a multidimensional uncertainty coupling probability lattice and the number M of coupling scenes according to the optimal clustering result; s4, according to the multidimensional uncertainty coupling probability lattice, reducing the number M of the coupling scenes into N typical daily scenes, and weighting and equivalent N typical day show scenes into typical month scenes according to scene probability; s5, sequentially carrying out decomposition operation on wind power and photovoltaic curve data in a typical month scene by adopting a moving average filtering algorithm; and S6, based on the decomposition data, constructing an electric hydrogen energy supplementing station optimal configuration model considering hydrogen energy transregional scheduling and electricity changing hydrogenation user perception by taking the lowest monthly comprehensive operation cost of the energy supplementing station as an optimization target, and obtaining a configuration scheduling strategy of the energy supplementing station by adopting Gurobi to solve a configuration result. In the step S2, clustering is carried out according to a time-of-day scale by adopting a K-means clustering algorithm, and four independent probability scenes are expressed as a set form: Wind power scenes { (P w,i(t), pw,i)| 1≤t≤T, 1≤i≤Kw }, wherein P w,i (T) is a daily power curve of wind power in an ith scene, P w,i is a probability of the ith scene of wind power, K w is an optimal clustering number of the wind power scenes, T is a daily period index, and T represents the maximum value of the daily period index T; The photovoltaic scene { (P pv,j(t), ppv,j)| 1≤t≤T, 1≤j≤Kpv }, wherein P pv,j (t) is a solar power curve of the photovoltaic in a j-th scene, P pv,j is light Fu Di j scene probabilities, and K pv is the optimal clustering number of the photovoltaic scenes; The power change scene is { (P s,k(t), ps,k)| 1≤t≤T, 1≤k≤Ks }, wherein P s,k (t) is a daily power curve of power change in the kth scene, P s,k is the probability of the kth scene of power change, and K s is the optimal clustering number of the power change scene; the hydrogenation scene { (P h,l(t), ph,l)| 1≤t≤T, 1≤l≤Kh }, wherein P h,l (t) is a daily power curve of hydrogenation in the first scene, P h,l is the probability of the first scene of hydrogenation, and K h is the optimal cluster number of the hydrogenation scene. In step S3, the multidimensional uncertainty coup